The present disclosure relates to systems and methods for magnetic resonance imaging (“MRI”). More particularly, the disclosure relates to systems and methods for accelerating data acquisitions used in magnetic resonance fingerprinting applications.
Magnetic resonance fingerprinting (“MRF”) is an imaging technique that enables quantitative mapping of tissue or other material properties based on random or pseudorandom measurements of the subject or object being imaged. Examples of parameters that can be mapped include longitudinal relaxation time, T1; transverse relaxation time, T2; main magnetic field map, B0; and proton density, ρ. MRF is generally described in U.S. Pat. No. 8,723,518, which is herein incorporated by reference in its entirety.
The data acquired with MRF techniques are compared with a dictionary of signal models, or templates, that have been generated for different acquisition parameters from magnetic resonance signal models, such as Bloch equation-based physics simulations. This comparison allows estimation of the desired physical parameters, such as those mentioned above. The parameters for the tissue or other material in a given voxel are estimated to be the values that provide the best signal template matching.
Often, a slice-selective, highly undersampled spiral k-space acquisition is utilized for two-dimensional MRF acquisitions, where in many instances, the spiral trajectory is changed from one time point (e.g., TR period) to the next. To enable accurate parameter estimation, for each imaging slice upwards of 1000-2000 time points are acquired with a TR that is typically about 10 milliseconds. This results in an acquisition time of around 10-20 seconds per imaging slice. To create high-resolution volumetric parameter maps with 1 mm slice thickness, approximately 120 imaging slices will have to be imaged, resulting in a total acquisition time of 20-40 minutes. This acquisition time is quite lengthy and limits the widespread clinical usage of MRF techniques.
In addition to lengthy acquisition time, MRF using a spiral k-space trajectory requires a complicated algorithm for image reconstruction and is not available on many clinical systems. Even when supported, the resulting images are plagued by imaging artifacts. Further, the resulting images do not provide quantitative information about the patient, which is an increasingly desired feature in clinical settings.
Ultra-low-field imaging (ULF) is an MRI variation born from the need to reduce the high-cost (e.g., $1,000,000 per tesla (T) of magnetic field) and scanner sitting requirements of traditional MRI. ULF provides a more affordable (<$50,000) option for smaller clinics and robust portable devices for hospital emergency departments. Unfortunately, however, the images produced from this technology provide limited 3D coverage and suffer from low signal to noise ratio (SNR) and poor resolution, limiting their use as a diagnostic tool. In addition, ULF MRI suffers from intrinsically long acquisition times, most of which is incompressible, that result from the time needed to generate nuclear polarization using smaller magnetic fields.
Thus, it would be desirable to provide a system and methods for magnetic resonance fingerprinting that is simple and robust, uses an optimized k-space trajectory, and produces 3D images.